Energies 2011, 4(3), 532-544; doi:10.3390/en4030532
Article

Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms

1,* email, 2email, 1email, 1email and 1email
Received: 13 January 2011; in revised form: 20 February 2011 / Accepted: 10 March 2011 / Published: 18 March 2011
(This article belongs to the Special Issue Hybrid Vehicles)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Compared to conventional vehicles Hybrid Electric Vehicles (HEVs) provide fairly high fuel economy with lower emissions. To enhance HEV performance in terms of fuel economy and emissions, and ensure user satisfaction with driving performance, the need for simultaneous optimization for the main parameters of powertrain components and control system is inevitable. However, this problem is challenging due to the large amount of coupling design parameters, conflicting design objectives and nonlinear constraints. Considering the defect of the methods which convert multi-objective optimization problems into single-objective ones, a comprehensive methodology based on the non-dominated sorting genetic algorithms II (NSGA II) to achieve parameter optimization for powertrain components and control system simultaneously and successfully find the Pareto-optimal solutions set is presented in this paper. A case simulation is carried out and simulated by ADVISOR, The simulation results show that this method can produce many Pareto-optimal solutions and a satisfactory solution can be selected by decision-makers according to their requirements. The results demonstrate the effectiveness of the algorithms proposed in this paper.
Keywords: hybrid electric vehicles; simultaneous optimization; multi-objective genetic algorithms; Pareto optimal solution
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MDPI and ACS Style

Fang, L.; Qin, S.; Xu, G.; Li, T.; Zhu, K. Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms. Energies 2011, 4, 532-544.

AMA Style

Fang L, Qin S, Xu G, Li T, Zhu K. Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms. Energies. 2011; 4(3):532-544.

Chicago/Turabian Style

Fang, Lincun; Qin, Shiyin; Xu, Gang; Li, Tianli; Zhu, Kemin. 2011. "Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms." Energies 4, no. 3: 532-544.

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